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mcp-forge

by carloseduardoscsouza-dot
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Python
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list_tables

Retrieve all user-defined SQLite tables to inspect available data before writing queries, describing schemas, or configuring a database connection.

Instructions

Lists all user tables in the SQLite database.

Tool call template

This tool declares no required parameters. Add optional parameters from the input schema as needed.

{
  "id": 1,
  "jsonrpc": "2.0",
  "method": "tools/call",
  "params": {
    "arguments": {},
    "name": "list_tables"
  }
}

Input Schema

NameRequiredDescriptionDefault

No arguments

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv1.0.0

TDQS

A3.7/5.0
Behavior3/5

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

With no annotations, the description carries the full burden. It does add real behavioral information by scoping the result to "user tables," implying system/internal tables are excluded, and "Lists" implies a read-only operation. However, it says nothing about return format, ordering, or whether views and indexes appear, so the disclosure is partial.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness5/5

Is the description appropriately sized, front-loaded, and free of redundancy?

A single front-loaded sentence with the scope qualifier ("user tables") placed before the database context. Nothing is redundant or padded.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness4/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

For a zero-parameter listing tool with no output schema, the description covers what the tool returns and its scope. The remaining gap is the shape of the response (bare names vs. rows with metadata) and whether non-table objects are included, which an agent might want to know.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters4/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

The tool takes zero parameters, so there is nothing for the description to disambiguate; baseline 4 applies. The description correctly signals that no filtering arguments exist by saying "all user tables."

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose4/5

Does the description clearly state what the tool does and how it differs from similar tools?

States a specific verb ("Lists") and a specific resource ("user tables in the SQLite database"), so an agent can tell this is a schema-discovery call rather than a data query. It does not explicitly contrast itself with describe_table or execute_query, so it stops short of a 5.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines3/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

Usage is only implied: "all user tables" suggests a first-step discovery call before describe_table or execute_query, but the description never states when to use it or when another tool is more appropriate. No exclusions or prerequisites are given.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

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